ComfyUI - 使用 ComfyUI 部署与测试 FLUX.1 图像生成模型 教程

前言

FLUX.1 是由 Black Forest Labs 推出的文本到图像生成模型,已成为 AI 绘画领域的高品质模型。该模型由 Stability AI 的前核心成员开发,具备强大的生成能力和高质量的图像输出。目前,Flux 的相关模型:

  • Flux & AE 模型:https://huggingface.co/black-forest-labs/FLUX.1-dev
  • CLIP 模型:https://huggingface.co/stabilityai/stable-diffusion-3-medium

FP8 模型地址:https://huggingface.co/Kijai/flux-fp8/tree/main

安装 HuggingFace 下载工具,使用镜像下载速度明显加快:

export HF_ENDPOINT="https://hf-mirror.com"
pip install -U huggingface_hub hf-transfer

下载 HuggingFace 脚本,如下:

huggingface-cli download --token [your toekn] black-forest-labs/FLUX.1-dev --local-dir FLUX.1-dev --include "flux1-dev.safetensors" 
huggingface-cli download --token [your toekn] black-forest-labs/FLUX.1-dev --local-dir FLUX.1-dev --include "ae.safetensors"
huggingface-cli download --token [your toekn] stabilityai/stable-diffusion-3-medium --local-dir stable-diffusion-3-medium 

下载之前需要申请权限,Token 地址来源于:全部勾选即可生成。

其中,完全版是 FP16 的版本 flux1-dev.safetensors,大约 23G,即:

FLUX.1-dev/flux1-dev.safetensors 
bypy upload flux1-dev.safetensors /stable_diffusion/flux_data/flux1-dev.safetensors

AE 模型:

FLUX.1-dev/ae.safetensors

CLIP 是两个模型:

stable-diffusion-3-medium/text_encoders/clip_l.safetensors
stable-diffusion-3-medium/text_encoders/t5xxl_fp16.safetensors

将 Flux 的相关模型,放入相应的位置:

ComfyUI/models/unet/flux1-dev.safetensors
ComfyUI/models/vae/ae.safetensors
ComfyUI/models/clip/clip_l.safetensors
ComfyUI/models/clip/t5xxl_fp16.safetensors

所有的AI设计工具,模型和插件,都已经整理好了,👇获取~在这里插入图片描述

搭建 Flux 流程:

Pipeline

相关提示词:

A queen sits on a throne, looking at the camera with arrogance, legs crossed, wearing high heels and white stockings.
The palace is bright, highlighting the queen’s majesty.
Low POV (Point of View), tilted shot from below.

Image

A Chinese queen sits on a very high throne in bright palace, looking at the camera, legs crossed, wearing golden high heels and black stockings.
very low POV, tilted shot from below.

Image

A woman empress of China sits on a very high throne in the bright palace. She is looking at the camera, legs crossed, wearing golden high heels and black stockings.
very low POV, tilted shot from very below.
very high quality.

Image
原图:
Image
重绘图像:
Image
ComfyUI Pipeline Json

{"last_node_id": 66,"last_link_id": 110,"nodes": [{"id": 8,"type": "UNETLoader","pos": [-372,-138],"size": {"0": 315,"1": 82},"flags": {},"order": 0,"mode": 0,"outputs": [{"name": "MODEL","type": "MODEL","links": [73],"shape": 3,"label": "模型","slot_index": 0}],"properties": {"Node name for S&R": "UNETLoader"},"widgets_values": ["flux1-dev.safetensors","default"]},{"id": 9,"type": "DualCLIPLoader","pos": [-372,164],"size": {"0": 315,"1": 106},"flags": {},"order": 1,"mode": 0,"outputs": [{"name": "CLIP","type": "CLIP","links": [81],"shape": 3,"label": "CLIP","slot_index": 0}],"properties": {"Node name for S&R": "DualCLIPLoader"},"widgets_values": ["t5xxl_fp16.safetensors","clip_l.safetensors","flux"]},{"id": 12,"type": "CLIPTextEncodeFlux","pos": [5,165],"size": {"0": 246.04954528808594,"1": 111.51980590820312},"flags": {},"order": 11,"mode": 0,"inputs": [{"name": "clip","type": "CLIP","link": 81,"label": "CLIP"},{"name": "clip_l","type": "STRING","link": 34,"widget": {"name": "clip_l"},"label": "CLIP_L","slot_index": 1},{"name": "t5xxl","type": "STRING","link": 35,"widget": {"name": "t5xxl"},"label": "T5XXL"}],"outputs": [{"name": "CONDITIONING","type": "CONDITIONING","links": [14],"shape": 3,"label": "条件","slot_index": 0}],"properties": {"Node name for S&R": "CLIPTextEncodeFlux"},"widgets_values": ["A queen sits on a throne, looking at the camera with arrogance, legs crossed, wearing high heels and black stockings.\nLow POV, tilted shot from below.\nThe palace is bathed in natural light.","A queen sits on a throne, looking at the camera with arrogance, legs crossed, wearing high heels and black stockings.\nLow POV, tilted shot from below.\nThe palace is bathed in natural light.",3.5,true,true]},{"id": 15,"type": "LatentFromBatch","pos": [317,135],"size": {"0": 315,"1": 82},"flags": {},"order": 9,"mode": 0,"inputs": [{"name": "samples","type": "LATENT","link": 13,"label": "Latent"}],"outputs": [{"name": "LATENT","type": "LATENT","links": [18],"shape": 3,"label": "Latent","slot_index": 0}],"properties": {"Node name for S&R": "LatentFromBatch"},"widgets_values": [0,1]},{"id": 5,"type": "EmptyLatentImage","pos": [-372,-291],"size": {"0": 315,"1": 106},"flags": {},"order": 2,"mode": 0,"outputs": [{"name": "LATENT","type": "LATENT","links": [13],"shape": 3,"label": "Latent","slot_index": 0}],"properties": {"Node name for S&R": "EmptyLatentImage"},"widgets_values": [1280,800,1]},{"id": 16,"type": "BasicScheduler","pos": [317,279],"size": {"0": 315,"1": 106},"flags": {},"order": 12,"mode": 0,"inputs": [{"name": "model","type": "MODEL","link": 79,"label": "模型"}],"outputs": [{"name": "SIGMAS","type": "SIGMAS","links": [20],"shape": 3,"label": "Sigmas","slot_index": 0}],"properties": {"Node name for S&R": "BasicScheduler"},"widgets_values": ["normal",25,1]},{"id": 11,"type": "ModelSamplingFlux","pos": [-372,-10],"size": {"0": 315,"1": 130},"flags": {},"order": 8,"mode": 0,"inputs": [{"name": "model","type": "MODEL","link": 73,"label": "模型"}],"outputs": [{"name": "MODEL","type": "MODEL","links": [78,79,99],"shape": 3,"label": "模型","slot_index": 0}],"properties": {"Node name for S&R": "ModelSamplingFlux"},"widgets_values": [1.15,0.5,1024,1024]},{"id": 61,"type": "Reroute","pos": [997,-64],"size": [75,26],"flags": {},"order": 13,"mode": 0,"inputs": [{"name": "","type": "*","link": 99}],"outputs": [{"name": "","type": "MODEL","links": [100],"slot_index": 0}],"properties": {"showOutputText": false,"horizontal": false}},{"id": 17,"type": "BasicGuider","pos": [317,-58],"size": {"0": 241.79998779296875,"1": 46},"flags": {},"order": 14,"mode": 0,"inputs": [{"name": "model","type": "MODEL","link": 78,"label": "模型"},{"name": "conditioning","type": "CONDITIONING","link": 14,"label": "条件"}],"outputs": [{"name": "GUIDER","type": "GUIDER","links": [17,101],"shape": 3,"label": "引导","slot_index": 0}],"properties": {"Node name for S&R": "BasicGuider"}},{"id": 14,"type": "KSamplerSelect","pos": [317,29],"size": {"0": 315,"1": 58},"flags": {},"order": 3,"mode": 0,"outputs": [{"name": "SAMPLER","type": "SAMPLER","links": [19,103],"shape": 3,"label": "采样器","slot_index": 0}],"properties": {"Node name for S&R": "KSamplerSelect"},"widgets_values": ["euler"]},{"id": 19,"type": "VAEDecode","pos": [717,-64],"size": {"0": 210,"1": 46},"flags": {"collapsed": false},"order": 17,"mode": 0,"inputs": [{"name": "samples","type": "LATENT","link": 93,"label": "Latent"},{"name": "vae","type": "VAE","link": 22,"label": "VAE"}],"outputs": [{"name": "IMAGE","type": "IMAGE","links": [89],"shape": 3,"label": "图像","slot_index": 0}],"properties": {"Node name for S&R": "VAEDecode"}},{"id": 10,"type": "VAELoader","pos": [-372,321],"size": {"0": 315,"1": 58},"flags": {},"order": 4,"mode": 0,"outputs": [{"name": "VAE","type": "VAE","links": [22,104],"shape": 3,"slot_index": 0,"label": "VAE"}],"properties": {"Node name for S&R": "VAELoader"},"widgets_values": ["flux_ae.safetensors"]},{"id": 63,"type": "Reroute","pos": [999,-33],"size": [75,26],"flags": {},"order": 10,"mode": 0,"inputs": [{"name": "","type": "*","link": 104}],"outputs": [{"name": "","type": "VAE","links": [105],"slot_index": 0}],"properties": {"showOutputText": false,"horizontal": false}},{"id": 62,"type": "VAEDecode","pos": [1135,92],"size": {"0": 210,"1": 46},"flags": {"collapsed": false},"order": 21,"mode": 0,"inputs": [{"name": "samples","type": "LATENT","link": 106,"label": "Latent"},{"name": "vae","type": "VAE","link": 105,"label": "VAE"}],"outputs": [{"name": "IMAGE","type": "IMAGE","links": [107],"shape": 3,"label": "图像","slot_index": 0}],"properties": {"Node name for S&R": "VAEDecode"}},{"id": 66,"type": "RandomNoise","pos": [1458,-350],"size": {"0": 315,"1": 82},"flags": {},"order": 5,"mode": 0,"outputs": [{"name": "NOISE","type": "NOISE","links": [110],"shape": 3,"label": "噪波生成","slot_index": 0}],"properties": {"Node name for S&R": "RandomNoise"},"widgets_values": [858548031216041,"randomize"]},{"id": 18,"type": "SamplerCustomAdvanced","pos": [713,-219],"size": {"0": 355.20001220703125,"1": 106},"flags": {},"order": 16,"mode": 0,"inputs": [{"name": "noise","type": "NOISE","link": 16,"label": "噪波生成"},{"name": "guider","type": "GUIDER","link": 17,"label": "引导"},{"name": "sampler","type": "SAMPLER","link": 19,"label": "采样器"},{"name": "sigmas","type": "SIGMAS","link": 20,"label": "Sigmas"},{"name": "latent_image","type": "LATENT","link": 18,"label": "Latent"}],"outputs": [{"name": "output","type": "LATENT","links": [93,108],"shape": 3,"label": "输出","slot_index": 0},{"name": "denoised_output","type": "LATENT","links": [],"shape": 3,"label": "降噪输出","slot_index": 1}],"properties": {"Node name for S&R": "SamplerCustomAdvanced"},"color": "#322","bgcolor": "#533"},{"id": 13,"type": "RandomNoise","pos": [317,-186],"size": {"0": 315,"1": 82},"flags": {},"order": 6,"mode": 0,"outputs": [{"name": "NOISE","type": "NOISE","links": [16],"shape": 3,"label": "噪波生成","slot_index": 0}],"properties": {"Node name for S&R": "RandomNoise"},"widgets_values": [3,"fixed"]},{"id": 37,"type": "StringFunction|pysssss","pos": [-373,442],"size": {"0": 403.92303466796875,"1": 274},"flags": {},"order": 7,"mode": 0,"outputs": [{"name": "STRING","type": "STRING","links": [34,35],"shape": 3,"label": "字符串","slot_index": 0}],"properties": {"Node name for S&R": "StringFunction|pysssss"},"widgets_values": ["append","yes","A woman empress of China sits on a very high throne in the bright palace. She is looking at the camera, legs crossed, wearing golden high heels and black stockings.\n","very low POV, tilted shot from very below.\nvery high quality.","","A woman empress of China sits on a very high throne in the bright palace. She is looking at the camera, legs crossed, wearing golden high heels and black stockings.\n, very low POV, tilted shot from very below.\nvery high quality."]},{"id": 65,"type": "LatentUpscaleBy","pos": [1127,-350],"size": {"0": 315,"1": 82},"flags": {},"order": 18,"mode": 0,"inputs": [{"name": "samples","type": "LATENT","link": 108,"label": "Latent"}],"outputs": [{"name": "LATENT","type": "LATENT","links": [109],"shape": 3,"label": "Latent","slot_index": 0}],"properties": {"Node name for S&R": "LatentUpscaleBy"},"widgets_values": ["nearest-exact",1.5]},{"id": 59,"type": "SamplerCustomAdvanced","pos": [1126,-219],"size": {"0": 355.20001220703125,"1": 106},"flags": {},"order": 20,"mode": 0,"inputs": [{"name": "noise","type": "NOISE","link": 110,"label": "噪波生成"},{"name": "guider","type": "GUIDER","link": 101,"label": "引导"},{"name": "sampler","type": "SAMPLER","link": 103,"label": "采样器"},{"name": "sigmas","type": "SIGMAS","link": 98,"label": "Sigmas"},{"name": "latent_image","type": "LATENT","link": 109,"label": "Latent"}],"outputs": [{"name": "output","type": "LATENT","links": [106],"shape": 3,"label": "输出","slot_index": 0},{"name": "denoised_output","type": "LATENT","links": [],"shape": 3,"label": "降噪输出","slot_index": 1}],"properties": {"Node name for S&R": "SamplerCustomAdvanced"},"color": "#322","bgcolor": "#533"},{"id": 60,"type": "BasicScheduler","pos": [1130,-63],"size": {"0": 315,"1": 106},"flags": {},"order": 15,"mode": 0,"inputs": [{"name": "model","type": "MODEL","link": 100,"label": "模型","slot_index": 0}],"outputs": [{"name": "SIGMAS","type": "SIGMAS","links": [98],"shape": 3,"label": "Sigmas","slot_index": 0}],"properties": {"Node name for S&R": "BasicScheduler"},"widgets_values": ["normal",20,0.5]},{"id": 7,"type": "PreviewImage","pos": [716,30],"size": {"0": 359.57421875,"1": 249.54296875},"flags": {},"order": 19,"mode": 0,"inputs": [{"name": "images","type": "IMAGE","link": 89,"label": "图像"}],"properties": {"Node name for S&R": "PreviewImage"}},{"id": 64,"type": "PreviewImage","pos": [1137,190],"size": {"0": 359.57421875,"1": 249.54296875},"flags": {},"order": 22,"mode": 0,"inputs": [{"name": "images","type": "IMAGE","link": 107,"label": "图像"}],"properties": {"Node name for S&R": "PreviewImage"}}],"links": [[13,5,0,15,0,"LATENT"],[14,12,0,17,1,"CONDITIONING"],[16,13,0,18,0,"NOISE"],[17,17,0,18,1,"GUIDER"],[18,15,0,18,4,"LATENT"],[19,14,0,18,2,"SAMPLER"],[20,16,0,18,3,"SIGMAS"],[22,10,0,19,1,"VAE"],[34,37,0,12,1,"STRING"],[35,37,0,12,2,"STRING"],[73,8,0,11,0,"MODEL"],[78,11,0,17,0,"MODEL"],[79,11,0,16,0,"MODEL"],[81,9,0,12,0,"CLIP"],[89,19,0,7,0,"IMAGE"],[93,18,0,19,0,"LATENT"],[98,60,0,59,3,"SIGMAS"],[99,11,0,61,0,"*"],[100,61,0,60,0,"MODEL"],[101,17,0,59,1,"GUIDER"],[103,14,0,59,2,"SAMPLER"],[104,10,0,63,0,"*"],[105,63,0,62,1,"VAE"],[106,59,0,62,0,"LATENT"],[107,62,0,64,0,"IMAGE"],[108,18,0,65,0,"LATENT"],[109,65,0,59,4,"LATENT"],[110,66,0,59,0,"NOISE"]],"groups": [],"config": {},"extra": {"ds": {"scale": 1.4864362802414455,"offset": [-818.3005016409714,-63.35849743821387]}},"version": 0.4
}

为了帮助大家更好地掌握 ComfyUI,我在去年花了几个月的时间,撰写并录制了一套ComfyUI的基础教程,共六篇。这套教程详细介绍了选择ComfyUI的理由、其优缺点、下载安装方法、模型与插件的安装、工作流节点和底层逻辑详解、遮罩修改重绘/Inpenting模块以及SDXL工作流手把手搭建。

由于篇幅原因,本文精选几个章节,详细版点击下方卡片免费领取

在这里插入图片描述

一、ComfyUI配置指南

  • 报错指南
  • 环境配置
  • 脚本更新
  • 后记

img

二、ComfyUI基础入门

  • 软件安装篇
  • 插件安装篇

img

三、 ComfyUI工作流节点/底层逻辑详解

  • ComfyUI 基础概念理解
  • Stable diffusion 工作原理
  • 工作流底层逻辑
  • 必备插件补全

img

四、ComfyUI节点技巧进阶/多模型串联

  • 节点进阶详解
  • 提词技巧精通
  • 多模型节点串联

img

五、ComfyUI遮罩修改重绘/Inpenting模块详解

  • 图像分辨率
  • 姿势

img

六、ComfyUI超实用SDXL工作流手把手搭建

  • Refined模型
  • SDXL风格化提示词
  • SDXL工作流搭建

img

由于篇幅原因,本文精选几个章节,详细版点击下方卡片免费领取

在这里插入图片描述

img

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